交通监控系统车牌识别方法研究
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摘要
机动车牌照识别(LPR)是智能交通系统(ITS)的关键组成部分。LPR在旅行时间管理,停车场管理,收费站,超速监测执法,冲闯红灯监测执法和被盗车辆识别等交通运输系统应用中扮演了重要的角色。尽管已有相关的研究工作正在开展,但在车牌照定位准确率,字符识别率等方面仍然有较大提升空间。为此,本课题重点关注车牌照识别准确率的研究,并期望力求在识别精度上能有进一步提高。
论文提出了一种新颖的机动车牌照识别方法以解决上述问题,该方法是一种基于形态学运算和图像投影算法的车牌照区域定位方法。论文首先应用Robert算子检测车牌照边缘,并结合统计学算法,去除车牌照的边界,边框和固定螺丝等干扰因素;最终利用水平和垂直投影方法抽取出完整的及去除干扰因素的车牌区域。然后使用区域生长算法对候选车牌区域进行分割;最后,利用逆向逻辑人工神经网络(RLANN)实现字符识别,并针对特殊字符,提出利用特征映射方法进行处理。鉴于车牌识别的复杂性,论文提出利用基于机动车牌照的先验知识和区域生长算法的字符分割技术和基于反向传播神经网络及其归一化字符识别技术,有效地提高车牌字符识别精度。
论文利用MATLAB 9.1软件对所提出的算法进行了实验仿真,通过对不同环境下的车牌照图像样本进行仿真实验,结果表明采用本文提出的算法用于机动车牌照识别是可行的,并且准确率和适应性较好。
License plate recognition (LPR) is the crucial component of the Intelligent Transport System (ITS). LPR plays important role in transport application such as travel time management, parking lot traffic management, speed limit enforcement, red light violation, identification of the stolen car etc. Lots of work has been done but there is still room for the improvement such as the license plate positioning ratio, character recognition ratio and so forth. As a result, the research intends to focus on this area in anticipation that the recognition accuracy of the license plate recognition system can be further enhanced.
In this thesis, innovative methods are proposed for LPR that is targeted to solve the inherited issues. Modified and effective method of license plate area location is presented which is based on the morphological and image projection technology. At first Robert edge is used to detect the vertical edge contained in the license plate. Then apply morphological operations to find the exact candidate of license plate and remove extra region which doesn’t belongs to plate. At last extract the plate region using horizontal and vertical image projection. Character segmentation is done using the prior knowledge of license plate and region growing algorithm. In proposed methodology at first, contrast stretching is performed to enhance the character region. Then to remove plate boundary, frame and rivet, row and column scan statistics algorithm has been applied. At last the candidate character is segmented by using region growing algorithm. This study focus on backpropagation neural networks learning paradigms and network framework combining normalization, parallel dispersed and reverse logical thinking concept to propose reverse logical artificial neural network (RLANN) for the character recognition.
The proposed algorithm was implemented on MATLAB 9.1. Many samples are taken in different environment. The experiments indicate that it is feasible to adopt this algorithm in LPR to achieve better accuracy and adaptability.
论文提出了一种新颖的机动车牌照识别方法以解决上述问题,该方法是一种基于形态学运算和图像投影算法的车牌照区域定位方法。论文首先应用Robert算子检测车牌照边缘,并结合统计学算法,去除车牌照的边界,边框和固定螺丝等干扰因素;最终利用水平和垂直投影方法抽取出完整的及去除干扰因素的车牌区域。然后使用区域生长算法对候选车牌区域进行分割;最后,利用逆向逻辑人工神经网络(RLANN)实现字符识别,并针对特殊字符,提出利用特征映射方法进行处理。鉴于车牌识别的复杂性,论文提出利用基于机动车牌照的先验知识和区域生长算法的字符分割技术和基于反向传播神经网络及其归一化字符识别技术,有效地提高车牌字符识别精度。
论文利用MATLAB 9.1软件对所提出的算法进行了实验仿真,通过对不同环境下的车牌照图像样本进行仿真实验,结果表明采用本文提出的算法用于机动车牌照识别是可行的,并且准确率和适应性较好。
License plate recognition (LPR) is the crucial component of the Intelligent Transport System (ITS). LPR plays important role in transport application such as travel time management, parking lot traffic management, speed limit enforcement, red light violation, identification of the stolen car etc. Lots of work has been done but there is still room for the improvement such as the license plate positioning ratio, character recognition ratio and so forth. As a result, the research intends to focus on this area in anticipation that the recognition accuracy of the license plate recognition system can be further enhanced.
In this thesis, innovative methods are proposed for LPR that is targeted to solve the inherited issues. Modified and effective method of license plate area location is presented which is based on the morphological and image projection technology. At first Robert edge is used to detect the vertical edge contained in the license plate. Then apply morphological operations to find the exact candidate of license plate and remove extra region which doesn’t belongs to plate. At last extract the plate region using horizontal and vertical image projection. Character segmentation is done using the prior knowledge of license plate and region growing algorithm. In proposed methodology at first, contrast stretching is performed to enhance the character region. Then to remove plate boundary, frame and rivet, row and column scan statistics algorithm has been applied. At last the candidate character is segmented by using region growing algorithm. This study focus on backpropagation neural networks learning paradigms and network framework combining normalization, parallel dispersed and reverse logical thinking concept to propose reverse logical artificial neural network (RLANN) for the character recognition.
The proposed algorithm was implemented on MATLAB 9.1. Many samples are taken in different environment. The experiments indicate that it is feasible to adopt this algorithm in LPR to achieve better accuracy and adaptability.
引文
[1] Jia Li, Mei Xie,“A Color and Texture Feature Based Approach to License Plate Location”, International Conference on Computational Intelligence and Security, 2007, Page(s): 376 - 380
[2] Tran Duc Duan, Duong Anh Duc, Tran Le Hong Du,“Combining Hough Transform and Contour Algorithm for detecting Vehicles License-Plates”, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Page(s): 747 - 750
[3] C. Nelson Kennedy Babu, Krishnan Nallaperumal,“A License Plate Localization using Morphology and Recognition”, India Conference, 2008, Page(s): 34 - 39
[4] Bai Hongliang, Liu Changping,“A hybrid License Plate Extraction Method Based on Edge Statistics and Morphology”, in proc. Conf. on Pattern Recognition (ICPR), 2004, pp. 831-834.
[5] Danian Zheng, Yannan Zhao, Jiaxin Wang,“An efficient method of license plate location”, Pattern Recognition Letters (2005), Volume 26, Issue 15, November 2005, Page(s): 2431-2438.
[6] SherrZheng Wang and HsMian Lee,“Detection and Recognition of License Plate Characters with Different Appearences”, in proc. Conf. Intelligent Transportation Systems, vol. 2, 2003, Page(s): 979– 984
[7] Xifan Shi, Weizhong Zhao, and Yonghang Shen,“Automatic License Plate Recognition System Based on Color Image Processing”, Lecture Notes on Computer Science 3483, O. Gervasi et al., Ed. Springer-Verlag, 2005, Page(s):1159-1168
[8] Dai Yan, Ma Hongqing, Liu Jilin and Li Langang,“A High Performance License Plate Recognition System Based on the Web Technique”, in proc. Conf. Intelligent Transportation Systems, 2001, Page(s): 325-329.
[9] Haijiao Wang, Xinnian Wang1, Wenju Li, Xiaodan Jia,“Color Prior Knowledge-Based License Plate Location Algorithm”Second Workshop on Digital Media and its Application in Museum & Heritage, 2007, Page(s): 47 - 52
[10] Paolo Comelli, Paolo Ferragina, Mario Notturno Granieri, and Flavio Stabile,“Optical Recognition of Motor Vehicle License Plates”, IEEE Transaction on vehicular technology, vol. 44, no.4, November 1995, Page(s): 790 - 799
[11] Jianxia Wang, Guilong Gao, Huili Yang,“Research and Implementation of License Plate Location Based on Histogram Division Method”, The Ninth International Conference on Electronic Measurement & Instruments, ICEMI’2009, Page(s): 1-230 - 1-233
[12] M. H. ter Brugge, J. H. Stevens, J.A. G. Nijhuis, L. Spaanenburg,“License Plate Recognition Using DTCNNs”, in proc. IEEE lnt. Workshop on Cellular Neural Networks and their Applications, 1998, Page(s): 212-217.
[13] Mario I. Chacon M., Alejandro Zimmerman S.,“License Plate Location Based on a Dynamic PCNN Scheme”, in proc. Int. Joint Conf. on Neural Networks, vol. 2, 2003, Pages(s): 1195– 1200.
[14] K.K.Kim K.I.Kim J.B.Kim H. J.Kim,“Learning-Based Approach, for License Plate Recognition”, in proc. IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing, Vol. 2, 2000, Page(s): 614 - 623.
[15] Rodolfo Zunino, Stefano Rovetta,“Vector Quantization for License-Plate Location and Image Coding”, IEEE transactions on industrial electronics, vol. 47, No. 1, February 2000, Page(s): 159 - 167
[16] Guangmin Sun, Gang Li, Lei Xu, Jing Wang,“An Effective Algorithm of Character Segmentation for Chinese License Plates with Different Colors”, Second International Symposium on Intelligent Information Technology Application, IEEE 2008, Page(s): 3 - 6
[17] Xiaodan Jia, Xinnian Wang, Wenju Li, Haijiao Wang,“A Novel Algorithm for Character Segmentation of Degraded License Plate Based on Prior Knowledge”, Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, 2007, Jinan, China, Page(s): 249 - 253
[18] Hui Li, Huanhuan Zhang,“Improved Projection Algorithm for License Plate Characters Segmentation”, Second International Conference on Intelligent Computation Technology and Automation, 2009, Page(s): 534 - 537
[19] Yungang Zhang, Changshui Zhang,“A New Algorithm for Character Segmentation of License Plate”, Intelligent Vehicles Symposium, 2003, Page(s): 106 - 109
[20] Feng Yang, Zheng Ma, Mei Xie,“A Novel Approach for License Plate Character Segmentation”, 1st IEEE conference on Industrial Electronics and Applications, 2006, Page(s): 1 - 6
[21] Shigueo Nomura, Keiji Yamanaka, Osamu Katai, Hiroshi Kawakami, Takayuki Shiose,“A novel adaptive morphological approach for degraded character image segmentation”, Pattern Recognition, Vol. 38, Issue 11, November 2005 , Page(s): 1961-1975
[22] C. de Trazegnies, C. Urdiales, A. Bandera, F. Sandoval,“A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences”, Image and Vision Computing, 2003, Page(s): 879–889
[23] Bing Feng, XiaoQing Ding,“Off-line Handwritten Chinese Character Recognition with Hidden Markov Models”, Proceedings of ICSP, 2000, Page(s): 1542 - 1545
[24] A.Broumandnia, M.Fathy,“Application of pattern recognition for Farsi license plate recognition”, ICGST International Conference on Graphics, Vision and Image Processing (GVIP-05), Page(s): 25-31
[25] Yo-Ping Huang,“A Back Propagation based Real-Time License Plate Recognition System”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 22, No. 2 (2008), Page(s): 233–251.
[26] Yuemei Xu, Hong Zhang,“Application of Improved BP Algorithm in Vehicle License Plate Recognition”, International Joint Conference on Artificial Intelligence, 2009, Page(s): 514 - 517
[27] Mohamed Deriche, Muhammad Moinuddin,“A New Approach for Recognizing Saudi Arabian License Plates using Neural Networks”, IEEEGCC 2007
[28] Qian Gao, Xinnian Wang and Gongfu Xie,“License Plate Recognition Based On Prior Knowledge”, Proceedings of the IEEE,International Conference on Automation and Logistics, 2007, Page(s): 2964 - 2968
[29] Bo Li, Zhi-yuan Zeng, Jian-zhong Zhou, Hua-li Dong,“An Algorithm for License Plate Recognition Using Radial Basis Function Neural Network”, International Symposium on Computer Science and Computational Technology, 2008, Page(s): 569 - 572
[30] Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen,“Automatic License Plate Recognition”, IEEE Trans. on Intelligent Transportation Systems, 2004, Page(s): 42-53
[31] Chen Yuan-yuan, Li Jing,“A Method of Number-plate Character Recognition Algorithm Based on Boosting Classification”, International Conference on Communications and Mobile Computing, 2009, Page(s): 263 - 267
[32] Cheokman Wu, Lei Chan On, Chan Hon Weng, Tong Sio Kuan, Kengchung Ng,“A Macao License Plate Recognition System”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, 2005, Page(s): 4506 - 4510
[33] Serkan Ozbay, Ergun Ercelebi,“Automatic Vehicle Identification by Plate Recognition”, World Academy of Science, Engineering and Technology, 2005, Page(s): 222-225
[34] Xiaojun Chil, Junyu Dong, Aihua Liu, Huiyu Zhou,“A Simple Method for Chinese License Plate Recognition Based on Support Vector Machine”, International Conference on Communication, circuit and system Proceeding, 2006, Page(s): 2141 - 2145
[35] Standard for vehicle license plate number in the People's Republic of China (GA 36-92).
[36] CheokMan WU, Kengchung NG,“A High Accurate Macau License Plate Recognition System”, Journal of Macau University of Science and Technology, 2008, Page(s): 23-28
[37] F. Martín, M. García, J. L. Alba,“New Methods for Automatic Reading of VLP's (Vehicle License Plates)”, International Conference Signal Processing, Pattern Recognition, and Applications, 2002.
[38] Zongyong Cui, Mei Xie,“A Method for Blue Background White Characters Car License Plate Location”, 2nd IEEE conference on Computer Science and Information Technology, 2009, Page(s): 393 - 395
[39] Meisen Pan, Junbiao Yan, Zhenghong Xiao“An Approach to Tilt Correction of Vehicle License Plate”, International Conference on Mechatronics and Automation, 2007, Page(s): 271 - 275
[40] Wisam Al Faqheri, Syamsiah Mashohor,“A Real-Time Malaysian Automatic License Plate Recognition (M-ALPR) using Hybrid Fuzzy”, IJCSNS International Journal of Computer Science and Network Security, February 2009, Page(s): 333-340
[41] Haidar Almohri, John S. Gray, Hisham Alnajjar,“A Real-time DSP-Based Optical Character Recognition System for Isolated Arabic characters using the TITMS320C6416T”, Proceedings of the IAJC-IJME International Conference, 2008
[42] Vmha Kamat ,Subramaniam Ganesan,“An Efficient Implementation of the Hough Transform for Detecting Vehicle License Plates Using DSP'S”, Proceedings on Real-Time Technology and Applications Symposium, 1995, Page(s): 58 - 59
[43] Meisen Pan, Junbiao Yan, Zhenghong Xiao,“An Approach to Tilt Correction of Vehicle License Plate”, International Conference on Mechatronics and Automation, 2007, Page(s): 271 - 275
[2] Tran Duc Duan, Duong Anh Duc, Tran Le Hong Du,“Combining Hough Transform and Contour Algorithm for detecting Vehicles License-Plates”, Proceedings of 2004 International Symposium on Intelligent Multimedia, Video and Speech Processing, Page(s): 747 - 750
[3] C. Nelson Kennedy Babu, Krishnan Nallaperumal,“A License Plate Localization using Morphology and Recognition”, India Conference, 2008, Page(s): 34 - 39
[4] Bai Hongliang, Liu Changping,“A hybrid License Plate Extraction Method Based on Edge Statistics and Morphology”, in proc. Conf. on Pattern Recognition (ICPR), 2004, pp. 831-834.
[5] Danian Zheng, Yannan Zhao, Jiaxin Wang,“An efficient method of license plate location”, Pattern Recognition Letters (2005), Volume 26, Issue 15, November 2005, Page(s): 2431-2438.
[6] SherrZheng Wang and HsMian Lee,“Detection and Recognition of License Plate Characters with Different Appearences”, in proc. Conf. Intelligent Transportation Systems, vol. 2, 2003, Page(s): 979– 984
[7] Xifan Shi, Weizhong Zhao, and Yonghang Shen,“Automatic License Plate Recognition System Based on Color Image Processing”, Lecture Notes on Computer Science 3483, O. Gervasi et al., Ed. Springer-Verlag, 2005, Page(s):1159-1168
[8] Dai Yan, Ma Hongqing, Liu Jilin and Li Langang,“A High Performance License Plate Recognition System Based on the Web Technique”, in proc. Conf. Intelligent Transportation Systems, 2001, Page(s): 325-329.
[9] Haijiao Wang, Xinnian Wang1, Wenju Li, Xiaodan Jia,“Color Prior Knowledge-Based License Plate Location Algorithm”Second Workshop on Digital Media and its Application in Museum & Heritage, 2007, Page(s): 47 - 52
[10] Paolo Comelli, Paolo Ferragina, Mario Notturno Granieri, and Flavio Stabile,“Optical Recognition of Motor Vehicle License Plates”, IEEE Transaction on vehicular technology, vol. 44, no.4, November 1995, Page(s): 790 - 799
[11] Jianxia Wang, Guilong Gao, Huili Yang,“Research and Implementation of License Plate Location Based on Histogram Division Method”, The Ninth International Conference on Electronic Measurement & Instruments, ICEMI’2009, Page(s): 1-230 - 1-233
[12] M. H. ter Brugge, J. H. Stevens, J.A. G. Nijhuis, L. Spaanenburg,“License Plate Recognition Using DTCNNs”, in proc. IEEE lnt. Workshop on Cellular Neural Networks and their Applications, 1998, Page(s): 212-217.
[13] Mario I. Chacon M., Alejandro Zimmerman S.,“License Plate Location Based on a Dynamic PCNN Scheme”, in proc. Int. Joint Conf. on Neural Networks, vol. 2, 2003, Pages(s): 1195– 1200.
[14] K.K.Kim K.I.Kim J.B.Kim H. J.Kim,“Learning-Based Approach, for License Plate Recognition”, in proc. IEEE Signal Processing Society Workshop, Neural Networks for Signal Processing, Vol. 2, 2000, Page(s): 614 - 623.
[15] Rodolfo Zunino, Stefano Rovetta,“Vector Quantization for License-Plate Location and Image Coding”, IEEE transactions on industrial electronics, vol. 47, No. 1, February 2000, Page(s): 159 - 167
[16] Guangmin Sun, Gang Li, Lei Xu, Jing Wang,“An Effective Algorithm of Character Segmentation for Chinese License Plates with Different Colors”, Second International Symposium on Intelligent Information Technology Application, IEEE 2008, Page(s): 3 - 6
[17] Xiaodan Jia, Xinnian Wang, Wenju Li, Haijiao Wang,“A Novel Algorithm for Character Segmentation of Degraded License Plate Based on Prior Knowledge”, Proceedings of the IEEE International Conference on Automation and Logistics August 18 - 21, 2007, Jinan, China, Page(s): 249 - 253
[18] Hui Li, Huanhuan Zhang,“Improved Projection Algorithm for License Plate Characters Segmentation”, Second International Conference on Intelligent Computation Technology and Automation, 2009, Page(s): 534 - 537
[19] Yungang Zhang, Changshui Zhang,“A New Algorithm for Character Segmentation of License Plate”, Intelligent Vehicles Symposium, 2003, Page(s): 106 - 109
[20] Feng Yang, Zheng Ma, Mei Xie,“A Novel Approach for License Plate Character Segmentation”, 1st IEEE conference on Industrial Electronics and Applications, 2006, Page(s): 1 - 6
[21] Shigueo Nomura, Keiji Yamanaka, Osamu Katai, Hiroshi Kawakami, Takayuki Shiose,“A novel adaptive morphological approach for degraded character image segmentation”, Pattern Recognition, Vol. 38, Issue 11, November 2005 , Page(s): 1961-1975
[22] C. de Trazegnies, C. Urdiales, A. Bandera, F. Sandoval,“A Hidden Markov Model object recognition technique for incomplete and distorted corner sequences”, Image and Vision Computing, 2003, Page(s): 879–889
[23] Bing Feng, XiaoQing Ding,“Off-line Handwritten Chinese Character Recognition with Hidden Markov Models”, Proceedings of ICSP, 2000, Page(s): 1542 - 1545
[24] A.Broumandnia, M.Fathy,“Application of pattern recognition for Farsi license plate recognition”, ICGST International Conference on Graphics, Vision and Image Processing (GVIP-05), Page(s): 25-31
[25] Yo-Ping Huang,“A Back Propagation based Real-Time License Plate Recognition System”, International Journal of Pattern Recognition and Artificial Intelligence, Vol. 22, No. 2 (2008), Page(s): 233–251.
[26] Yuemei Xu, Hong Zhang,“Application of Improved BP Algorithm in Vehicle License Plate Recognition”, International Joint Conference on Artificial Intelligence, 2009, Page(s): 514 - 517
[27] Mohamed Deriche, Muhammad Moinuddin,“A New Approach for Recognizing Saudi Arabian License Plates using Neural Networks”, IEEEGCC 2007
[28] Qian Gao, Xinnian Wang and Gongfu Xie,“License Plate Recognition Based On Prior Knowledge”, Proceedings of the IEEE,International Conference on Automation and Logistics, 2007, Page(s): 2964 - 2968
[29] Bo Li, Zhi-yuan Zeng, Jian-zhong Zhou, Hua-li Dong,“An Algorithm for License Plate Recognition Using Radial Basis Function Neural Network”, International Symposium on Computer Science and Computational Technology, 2008, Page(s): 569 - 572
[30] Shyang-Lih Chang, Li-Shien Chen, Yun-Chung Chung, and Sei-Wan Chen,“Automatic License Plate Recognition”, IEEE Trans. on Intelligent Transportation Systems, 2004, Page(s): 42-53
[31] Chen Yuan-yuan, Li Jing,“A Method of Number-plate Character Recognition Algorithm Based on Boosting Classification”, International Conference on Communications and Mobile Computing, 2009, Page(s): 263 - 267
[32] Cheokman Wu, Lei Chan On, Chan Hon Weng, Tong Sio Kuan, Kengchung Ng,“A Macao License Plate Recognition System”, Proceedings of the Fourth International Conference on Machine Learning and Cybernetics, 2005, Page(s): 4506 - 4510
[33] Serkan Ozbay, Ergun Ercelebi,“Automatic Vehicle Identification by Plate Recognition”, World Academy of Science, Engineering and Technology, 2005, Page(s): 222-225
[34] Xiaojun Chil, Junyu Dong, Aihua Liu, Huiyu Zhou,“A Simple Method for Chinese License Plate Recognition Based on Support Vector Machine”, International Conference on Communication, circuit and system Proceeding, 2006, Page(s): 2141 - 2145
[35] Standard for vehicle license plate number in the People's Republic of China (GA 36-92).
[36] CheokMan WU, Kengchung NG,“A High Accurate Macau License Plate Recognition System”, Journal of Macau University of Science and Technology, 2008, Page(s): 23-28
[37] F. Martín, M. García, J. L. Alba,“New Methods for Automatic Reading of VLP's (Vehicle License Plates)”, International Conference Signal Processing, Pattern Recognition, and Applications, 2002.
[38] Zongyong Cui, Mei Xie,“A Method for Blue Background White Characters Car License Plate Location”, 2nd IEEE conference on Computer Science and Information Technology, 2009, Page(s): 393 - 395
[39] Meisen Pan, Junbiao Yan, Zhenghong Xiao“An Approach to Tilt Correction of Vehicle License Plate”, International Conference on Mechatronics and Automation, 2007, Page(s): 271 - 275
[40] Wisam Al Faqheri, Syamsiah Mashohor,“A Real-Time Malaysian Automatic License Plate Recognition (M-ALPR) using Hybrid Fuzzy”, IJCSNS International Journal of Computer Science and Network Security, February 2009, Page(s): 333-340
[41] Haidar Almohri, John S. Gray, Hisham Alnajjar,“A Real-time DSP-Based Optical Character Recognition System for Isolated Arabic characters using the TITMS320C6416T”, Proceedings of the IAJC-IJME International Conference, 2008
[42] Vmha Kamat ,Subramaniam Ganesan,“An Efficient Implementation of the Hough Transform for Detecting Vehicle License Plates Using DSP'S”, Proceedings on Real-Time Technology and Applications Symposium, 1995, Page(s): 58 - 59
[43] Meisen Pan, Junbiao Yan, Zhenghong Xiao,“An Approach to Tilt Correction of Vehicle License Plate”, International Conference on Mechatronics and Automation, 2007, Page(s): 271 - 275